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In this work, we applied correlation-based feature selection (CFS), chi-squared, information gain, symmetrical uncertainty, and consistency filter methods to select the most relevant features from a 156-feature real dataset. This dataset contains clinical, serological, and nerve conduction tests data obtained from GBS patients. The most relevant feature subsets, determined with each filter method, were used to identify four subtypes of GBS present in the dataset. We used partitions around medoids (PAM) clustering algorithm to form four clusters, corresponding to the GBS subtypes. We applied the purity of each cluster as evaluation measure. After experimentation, symmetrical uncertainty and information gain determined a feature subset of seven variables. These variables conformed as a dataset were used as input to PAM and reached a purity of 0.7984. This result leads to a first characterization of this syndrome using computational techniques.<\/jats:p>","DOI":"10.1155\/2014\/432109","type":"journal-article","created":{"date-parts":[[2014,9,15]],"date-time":"2014-09-15T17:02:22Z","timestamp":1410800542000},"page":"1-9","source":"Crossref","is-referenced-by-count":10,"title":["Feature Selection for Better Identification of Subtypes of Guillain-Barr\u00e9 Syndrome"],"prefix":"10.1155","volume":"2014","author":[{"given":"Jos\u00e9","family":"Hern\u00e1ndez-Torruco","sequence":"first","affiliation":[{"name":"Divisi\u00f3n Acad\u00e9mica de Inform\u00e1tica y Sistemas, Universidad Ju\u00e1rez Aut\u00f3noma de Tabasco, Km. 1 Carretera Cunduac\u00e1n-Jalpa de M\u00e9ndez, Colonia La Esmeralda, 86690 Cunduac\u00e1n, TAB, 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